pith:VCL4BO3W
Differentiable hybrid force fields support scalable autonomous electrolyte discovery
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for autonomous electrolyte discovery.
arxiv:2604.07979 v2 · 2026-04-09 · cond-mat.mtrl-sci · physics.comp-ph
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Claims
Differentiable hybrid FFs resolve the trilemma of being fast enough for high-throughput screening, accurate enough for quantitative prediction, and calibratable enough for online refinement, thereby enabling closed-loop autonomous electrolyte discovery.
That the physical skeletons of these hybrid models provide a well-conditioned parameter space for differentiable molecular dynamics and that the cited models achieve reliable zero-shot generalization to bulk phases without post-hoc adjustments.
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for scalable autonomous electrolyte discovery.
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| First computed | 2026-06-09T01:04:42.617834Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
a897c0bb7649d5e9473957865642c48d52d19439c2bae98481642766c31e06d9
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/VCL4BO3WJHK6SRZZK6DFMQWERV \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a897c0bb7649d5e9473957865642c48d52d19439c2bae98481642766c31e06d9
Canonical record JSON
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